Normalize the variables

Now, for the last step in data preparation. You will transform the unskewed dataset wholesale_boxcox to the same scale, meaning all columns have a mean of zero, and standard deviation of 1. You will use the StandardScaler function from the sklearn.preprocessing module.

The unskewed wholesale_coxbox dataset you have transformed in the previous exercise has been imported as a pandas DataFrame. Also, the StandardScaler() instance has been initialized as scaler.

This exercise is part of the course

Machine Learning for Marketing in Python

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Exercise instructions

Fit the initialized scaler instance on the Box-Cox transformed dataset.
Transform and store the scaled dataset as wholesale_scaled.
Create a pandas DataFrame from the scaled dataset.
Print the mean and standard deviation for all columns.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Fit the initialized `scaler` instance on the Box-Cox transformed dataset
scaler.___(wholesale_boxcox)

# Transform and store the scaled dataset as `wholesale_scaled`
wholesale_scaled = scaler.___(wholesale_boxcox)

# Create a `pandas` DataFrame from the scaled dataset
wholesale_scaled_df = pd.DataFrame(data=___,
                                       index=wholesale_boxcox.___,
                                       columns=wholesale_boxcox.columns)

# Print the mean and standard deviation for all columns
print(wholesale_scaled_df.agg(['___','std']).round())

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